Visualizing collaboration data helps users to identify important contributors with expertise in various areas of interest. Some techniques use publication venues to represent areas of interest and, thus, datasets that are associated with a single venue or datasets that do not have well-defined venues cannot take full advantage of these techniques. Additionally, these techniques produce cluttered visualizations for datasets that include many venues or people, thus reducing the usefulness of these visualizations.
Therefore, there is a need for implementations that address these deficiencies in order to produce clear visualizations (e.g., less cluttered visualizations) that work with many types of datasets.